Imperial College London
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ProfessorNickOliver

Faculty of Medicine, Department of Metabolism, Digestion and Reproduction

Wynn Chair in Human Metabolism (Clinical)
 
 
 
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Contact

 

+44 (0)20 7594 1796nick.oliver

 
 
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Location

 

7S7aCommonwealth BuildingHammersmith Campus

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Summary

 

Publications

Citation

BibTex format

@article{Zhu:2022:10.1038/s41746-022-00626-5,
author = {Zhu, T and Uduku, C and Li, K and Herrero, Vinas P and Oliver, N and Georgiou, P},
doi = {10.1038/s41746-022-00626-5},
journal = {npj Digital Medicine},
title = {Enhancing self-management in type 1 diabetes with wearables and deep learning},
url = {http://dx.doi.org/10.1038/s41746-022-00626-5},
volume = {5},
year = {2022}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - People living with type 1 diabetes (T1D) require lifelong selfmanagement to maintain glucose levels in a safe range. Failure to do socan lead to adverse glycemic events with short and long-term complications. Continuous glucose monitoring (CGM) is widely used in T1Dself-management for real-time glucose measurements, while smartphoneapps are adopted as basic electronic diaries, data visualization tools, andsimple decision support tools for insulin dosing. Applying a mixed effectslogistic regression analysis to the outcomes of a six-week longitudinalstudy in 12 T1D adults using CGM and a clinically validated wearablesensor wristband (NCT ID: NCT03643692), we identified several significant associations between physiological measurements and hypo- andhyperglycemic events measured an hour later. We proceeded to developa new smartphone-based platform, ARISES (Adaptive, Real-time, and Intelligent System to Enhance Self-care), with an embedded deep learning algorithm utilizing multi-modal data from CGM, daily entries of mealand bolus insulin, and the sensor wristband to predict glucose levels andhypo- and hyperglycemia. For a 60-minute prediction horizon, the proposed algorithm achieved the average root mean square error (RMSE)of 35.28±5.77 mg/dL with the Matthews correlation coefficients fordetecting hypoglycemia and hyperglycemia of 0.56±0.07 and 0.70±0.05,respectively. The use of wristband data significantly reduced the RMSEby 2.25 mg/dL (p < 0.01). The well-trained model is implemented onthe ARISES app to provide real-time decision support. These resultsindicate that the ARISES has great potential to mitigate the risk ofsevere complications and enhance self-management for people with T1D.
AU  - Zhu,T
AU  - Uduku,C
AU  - Li,K
AU  - Herrero,Vinas P
AU  - Oliver,N
AU  - Georgiou,P
DO  - 10.1038/s41746-022-00626-5
PY  - 2022///
SN  - 2398-6352
TI  - Enhancing self-management in type 1 diabetes with wearables and deep learning
T2  - npj Digital Medicine
UR  - http://dx.doi.org/10.1038/s41746-022-00626-5
UR  - https://www.nature.com/articles/s41746-022-00626-5
UR  - http://hdl.handle.net/10044/1/97609
VL  - 5
ER  -
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